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"""Copyright 2024, XGBoost contributors"""
import json
import os
import tempfile
from typing import Type, Union
import numpy as np
import pytest
import xgboost as xgb
pl = pytest.importorskip("polars")
@pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
def test_polars_basic(
DMatrixT: Union[Type[xgb.DMatrix], Type[xgb.QuantileDMatrix]]
) -> None:
df = pl.DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]})
Xy = DMatrixT(df)
assert Xy.num_row() == df.shape[0]
assert Xy.num_col() == df.shape[1]
assert Xy.num_nonmissing() == np.prod(df.shape)
# feature info
assert Xy.feature_names == df.columns
assert Xy.feature_types == ["int", "int"]
res = Xy.get_data().toarray()
res1 = df.to_numpy()
if isinstance(Xy, xgb.QuantileDMatrix):
# skip min values in the cut.
np.testing.assert_allclose(res[1:, :], res1[1:, :])
else:
np.testing.assert_allclose(res, res1)
# boolean
df = pl.DataFrame({"a": [True, False, False], "b": [False, False, True]})
Xy = DMatrixT(df)
np.testing.assert_allclose(
Xy.get_data().data, np.array([1, 0, 0, 0, 0, 1]), atol=1e-5
)
def test_polars_missing() -> None:
df = pl.DataFrame({"a": [1, None, 3], "b": [3, 4, None]})
Xy = xgb.DMatrix(df)
assert Xy.num_row() == df.shape[0]
assert Xy.num_col() == df.shape[1]
assert Xy.num_nonmissing() == 4
np.testing.assert_allclose(Xy.get_data().data, np.array([1, 3, 4, 3]))
np.testing.assert_allclose(Xy.get_data().indptr, np.array([0, 2, 3, 4]))
np.testing.assert_allclose(Xy.get_data().indices, np.array([0, 1, 1, 0]))
ser = pl.Series("y", np.arange(0, df.shape[0]))
Xy.set_info(label=ser)
booster = xgb.train({}, Xy, num_boost_round=1)
predt0 = booster.inplace_predict(df)
predt1 = booster.predict(Xy)
np.testing.assert_allclose(predt0, predt1)
def test_classififer() -> None:
from sklearn.datasets import make_classification, make_multilabel_classification
X, y = make_classification(random_state=2024)
X_df = pl.DataFrame(X)
y_ser = pl.Series(y)
clf0 = xgb.XGBClassifier()
clf0.fit(X_df, y_ser)
clf1 = xgb.XGBClassifier()
clf1.fit(X, y)
with tempfile.TemporaryDirectory() as tmpdir:
path0 = os.path.join(tmpdir, "clf0.json")
clf0.save_model(path0)
path1 = os.path.join(tmpdir, "clf1.json")
clf1.save_model(path1)
with open(path0, "r") as fd:
model0 = json.load(fd)
with open(path1, "r") as fd:
model1 = json.load(fd)
model0["learner"]["feature_names"] = []
model0["learner"]["feature_types"] = []
assert model0 == model1
predt0 = clf0.predict(X)
predt1 = clf1.predict(X)
np.testing.assert_allclose(predt0, predt1)
assert (clf0.feature_names_in_ == X_df.columns).all()
assert clf0.n_features_in_ == X_df.shape[1]
X, y = make_multilabel_classification(128)
X_df = pl.DataFrame(X)
y_df = pl.DataFrame(y)
clf = xgb.XGBClassifier(n_estimators=1)
clf.fit(X_df, y_df)
assert clf.n_classes_ == 2
X, y = make_classification(n_classes=3, n_informative=5)
X_df = pl.DataFrame(X)
y_ser = pl.Series(y)
clf = xgb.XGBClassifier(n_estimators=1)
clf.fit(X_df, y_ser)
assert clf.n_classes_ == 3
def test_regressor() -> None:
from sklearn.datasets import make_regression
X, y = make_regression(n_targets=3)
X_df = pl.DataFrame(X)
y_df = pl.DataFrame(y)
assert y_df.shape[1] == 3
reg0 = xgb.XGBRegressor()
reg0.fit(X_df, y_df)
reg1 = xgb.XGBRegressor()
reg1.fit(X, y)
predt0 = reg0.predict(X)
predt1 = reg1.predict(X)
np.testing.assert_allclose(predt0, predt1)
def test_categorical() -> None:
import polars as pl
df = pl.DataFrame(
{"f0": [1, 2, 3], "b": ["a", "b", "c"]},
schema=[("a", pl.Int64()), ("b", pl.Categorical())]
)
with pytest.raises(NotImplementedError, match="Categorical feature"):
xgb.DMatrix(df, enable_categorical=True)
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